scholarly journals The Geometry of Continuous Latent Space Models for Network Data

2019 ◽  
Vol 34 (3) ◽  
pp. 428-453 ◽  
Author(s):  
Anna L. Smith ◽  
Dena M. Asta ◽  
Catherine A. Calder
Keyword(s):  
Network Data ◽  
Latent Space ◽  
Latent Space Models

scholarly journals Latent space models for multiview network data

2017 ◽  
Vol 11 (3) ◽  
pp. 1217-1244 ◽  
Author(s):  
Michael Salter-Townshend ◽  
Tyler H. McCormick
Keyword(s):  
Network Data ◽  
Latent Space ◽  
Latent Space Models

scholarly journals Statistical Inference in Political Networks Research

2016 ◽  
Author(s):  
Bruce A. Desmarais ◽  
Skyler J. Cranmer
Keyword(s):  
Analytic Form ◽  
Quadratic Assignment ◽  
Network Data ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
Political Networks ◽  
Latent Space ◽  
Latent Space Models

Researchers interested in statistically modeling network data have a well-established and quickly growing set of approaches from which to choose. Several of these methods have been regularly applied in research on political networks, while others have yet to permeate the field. This chapter reviews the most prominent methods of inferential network analysis for both cross-sectionally and longitudinally observed networks, including (temporal) exponential random graph models, latent space models, the quadratic assignment procedure, and stochastic actor oriented models. For each method, the chapter summarizes its analytic form, identifies prominent published applications in political science, and discusses computational considerations. It concludes with a set of guidelines for selecting a method for a given application.

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review

2021 ◽  
Vol 30 (1) ◽  
pp. 19-33
Author(s):  
Annis Shafika Amran ◽  
Sharifah Aida Sheikh Ibrahim ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Nurfaten Hamzah ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Linear Models ◽  
Brain Activity ◽  
Generative Models ◽  
Education Management ◽  
Latent Space ◽  
Latent Space Models ◽  
Cost Feasibility ◽  
Medical Healthcare ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

Modeling reciprocity in social interactions with probabilistic latent space models

2011 ◽  
Vol 17 (1) ◽  
pp. 1-36 ◽  
Author(s):  
ROXANA GIRJU ◽  
MICHAEL J. PAUL
Keyword(s):  
Gender Differences ◽  
Social Interactions ◽  
Pattern Discovery ◽  
Unsupervised Clustering ◽  
Structural Level ◽  
Latent Space ◽  
Affective Value ◽  
Latent Space Models ◽  
The Web

AbstractReciprocity is a pervasive concept that plays an important role in governing people's behavior, judgments, and thus their social interactions. In this paper we present an analysis of the concept of reciprocity as expressed in English and a way to model it. At a larger structural level the reciprocity model will induce representations and clusters of relations between interpersonal verbs. In particular, we introduce an algorithm that semi-automatically discovers patterns encoding reciprocity based on a set of simple yet effective pronoun templates. Using the most frequently occurring patterns we queried the web and extracted 13,443 reciprocal instances, which represent a broad-coverage resource. Unsupervised clustering procedures are performed to generate meaningful semantic clusters of reciprocal instances. We also present several extensions (along with observations) to these models that incorporate meta-attributes like the verbs' affective value, identify gender differences between participants, consider the textual context of the instances, and automatically discover verbs with certain presuppositions. The pattern discovery procedure yields an accuracy of 97 per cent, while the clustering procedures – clustering with pairwise membership and clustering with transitions – indicate accuracies of 91 per cent and 64 per cent, respectively. Our affective value clustering can predict an unknown verb's affective value (positive, negative, or neutral) with 51 per cent accuracy, while it can discriminate between positive and negative values with 68 per cent accuracy. The presupposition discovery procedure yields an accuracy of 97 per cent.

scholarly journals Deciphering protein evolution and fitness landscapes with latent space models

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinqiang Ding ◽  
Zhengting Zou ◽  
Charles L. Brooks III
Keyword(s):  
Protein Evolution ◽  
Fitness Landscape ◽  
Dimensional Space ◽  
Gaussian Process Regression ◽  
Protein Sequences ◽  
Fitness Landscapes ◽  
Space Representation ◽  
Latent Space ◽  
Low Dimensional ◽  
Latent Space Models

AbstractProtein sequences contain rich information about protein evolution, fitness landscapes, and stability. Here we investigate how latent space models trained using variational auto-encoders can infer these properties from sequences. Using both simulated and real sequences, we show that the low dimensional latent space representation of sequences, calculated using the encoder model, captures both evolutionary and ancestral relationships between sequences. Together with experimental fitness data and Gaussian process regression, the latent space representation also enables learning the protein fitness landscape in a continuous low dimensional space. Moreover, the model is also useful in predicting protein mutational stability landscapes and quantifying the importance of stability in shaping protein evolution. Overall, we illustrate that the latent space models learned using variational auto-encoders provide a mechanism for exploration of the rich data contained in protein sequences regarding evolution, fitness and stability and hence are well-suited to help guide protein engineering efforts.

scholarly journals Modeling node heterogeneity in latent space models for multidimensional networks

2020 ◽  
Vol 74 (3) ◽  
pp. 324-341
Author(s):  
Silvia D'Angelo ◽  
Marco Alfò ◽  
Thomas Brendan Murphy
Keyword(s):  
Latent Space ◽  
Latent Space Models

scholarly journals Maximum Likelihood Embedding of Logistic Random Dot Product Graphs

2020 ◽  
Vol 34 (04) ◽  
pp. 5289-5297
Author(s):  
Luke J. O'Connor ◽  
Muriel Medard ◽  
Soheil Feizi
Keyword(s):  
Maximum Likelihood ◽  
Spectral Method ◽  
Political Blogs ◽  
Space Model ◽  
Product Graphs ◽  
Latent Space ◽  
Dot Product ◽  
Latent Space Model ◽  
Exact Maximum Likelihood ◽  
Latent Space Models

A latent space model for a family of random graphs assigns real-valued vectors to nodes of the graph such that edge probabilities are determined by latent positions. Latent space models provide a natural statistical framework for graph visualizing and clustering. A latent space model of particular interest is the Random Dot Product Graph (RDPG), which can be fit using an efficient spectral method; however, this method is based on a heuristic that can fail, even in simple cases. Here, we consider a closely related latent space model, the Logistic RDPG, which uses a logistic link function to map from latent positions to edge likelihoods. Over this model, we show that asymptotically exact maximum likelihood inference of latent position vectors can be achieved using an efficient spectral method. Our method involves computing top eigenvectors of a normalized adjacency matrix and scaling eigenvectors using a regression step. The novel regression scaling step is an essential part of the proposed method. In simulations, we show that our proposed method is more accurate and more robust than common practices. We also show the effectiveness of our approach over standard real networks of the karate club and political blogs.

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